A Novel State Estimation Approach for Suspension System with Time-Varying and Unknown Noise Covariance

In this paper, a novel state estimation approach based on the variational Bayesian adaptive Kalman filter (VBAKF) and road classification is proposed for suspension system with time-varying unknown noise covariance. Using VB approach, covariance can be inferred from inverse-Wishart distribution then optimized by finite sampling posterior probability function (PDF) of backward smoothing. addition, new algorithm multi-objective optimization linear classifier to identify Simulation results model show that has higher performance in accuracy than other filters.